Explainable AI (XAI) methods have been proposed as a tool for assessing the reliability of algorithmic predictions  [1]}.
However, a limiting factor of existing XAI methods is that many are not specifically equipped to explain errors: predictions are explained without awareness of their correctness.
In practice, researchers have struggled to demonstrate that XAI methods allow users to recognize (un)trustworthy predictions [2]}.
Overall, current methods are insufficient to help users understand when, and for what reason, algorithmic predictions are (not) trustworthy in production settings where the true error is unknown.
On the one hand, confidence scores for individual predictions – when available – do not provide reasons for a measured low confidence.
On the other hand, most XAI methods do not differentiate between correct and incorrect predictions.
